MathCo Interview Questions, Process, and Tips

11 questions 10 MCQ + 1 pseudocode

Case study was good and the topic was semiconductor

Q1. Project description
Ans. 
The project involves developing a new software application for data analysis.
• Developing a software application for data analysis

• Utilizing programming languages such as Python and R

• Implementing statistical models and algorithms

• Creating visualizations and reports for data interpretation
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Q2. Project, ML & Stats
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Q1. Asked about scenario-based Small case study questions
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Q2. Explain statistical concepts like Hypothesis testing, and type 1 and type 2 errors.
Ans. 
Hypothesis testing is a statistical method to test a claim about a population parameter. Type 1 error is rejecting a true null hypothesis, and type 2 error is failing to reject a false null hypothesis.
• Hypothesis testing involves formulating a null hypothesis and an alternative hypothesis.

• Type 1 error occurs when we reject a null hypothesis that is actually true.

• Type 2 error occurs when we fail to reject a null hypothes...read more
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Q3. Coding questions about Python and SQL
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Q4. Questions about Machine learning algorithms, AUC ROC, Classification metrics
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Q1. Explain about projects working on.
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Q2. About a small scenario-based case study, how will you perform
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Q3. How will you handle angry clients
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Q1. Situation-based questions, small management scenario, what you will do
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Q2. More questions about you? what you like and how the organization is good for you
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Q1. Modelling questions
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Q2. Linear regression assumptions
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Linear regression assumptions include linearity, independence, homoscedasticity, and normality.
• Assumption of linearity: The relationship between the independent and dependent variables is linear.

• Assumption of independence: The residuals are independent of each other.

• Assumption of homoscedasticity: The variance of the residuals is constant across all levels of the independent variables.

• Assumption of normality: The resid...read more
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Q3. Tree based models
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Q1. Fitment round taken by HR
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Q1. Question on projects, the difference between call by value and call by reference, oops concepts,
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Q2. Aptitude questions
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Q1. Resume Screening.....
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Q1. Project explanation from the resume, NLP, Python, Supervised/Unsupervised/Boosting Algorithms
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Q1. Logical thinking.....
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